基于小波包最优节点能量特征的BA-ELM齿轮故障诊断方法  

BA-ELM Gear Fault Diagnosis Method based on Energy Feature of Wavelet Packet Optimal Node

作　　者：秦波[1] 刘永亮[1] 王建国[1] 秦岩[2] 杨云中 

机构地区：[1]内蒙古科技大学机械工程学院,内蒙古包头014010 [2]浙江大学信息学部控制科学与工程学院,浙江杭州310000

出　　处：《机械传动》2016年第4期33-37,共5页Journal of Mechanical Transmission

基　　金：国家自然科学基金(21366017);内蒙古自然科学基金(2015MS0512);内蒙古科技大学创新基金(2015QDL12)部分资助

摘　　要：针对表征齿轮故障特征信息难提取与极限学习机输入权值与隐含层节点阈值随机选取,致使齿轮故障分类模型泛化能力弱、精度差的问题,提出一种基于小波包最优节点能量特征的BA-ELM齿轮故障诊断方法。该方法首先将齿轮振动信号经过小波包分解,再利用分解所得各节点信号与原信号的相关系数选取出最优节点并计算其能量特征;其次,利用蝙蝠算法优化极限学习机的输入权值与隐含层节点阈值,建立BA-ELM的齿轮故障分类模型;最后,将所得小波包最优节点能量特征向量作为模型输入进行齿轮不同故障状态的分类识别。实验结果表明,与基于SVM和ELM的故障分类方法相比,基于小波包最优节点能量特征的BA-ELM齿轮故障诊断方法具有更高的分类精度,更强的泛化能力。In order to solve the problems that gear fault classification model has weak generalization ability,poor accuracy causing by the fault features of gear is difficult to extract and extreme learning machine input weights and threshold of hidden layer nodes randomly selected,a BA- ELM gear fault diagnosis method is puts forward based on energy feature of wavelet packet optimal nodes.First,the gear vibration signals are decomposed by using wavelet packet in this method,the optimal nodes is selected by using the correlation coefficient between each node decomposition signals and original signal,and the energy feature is calculated.Second,the bat algorithm is used to optimize the extreme learning machine input weights and threshold of hidden layer node and the gear fault classification model of BA-ELM is established.Finally,the energy entropy feature vectors of the optimal wavelet packet nodes as the model input is used to identify the different fault states of gear.The experimental results show that,comparing with SVM and ELM fault classification method,the BA-ELM gear fault diagnosis method based on energy feature of wavelet packet optimal nodes has higher classification accuracy and better generalization ability.

关 键 词：小波包 能量熵 蝙蝠算法 支持向量机 齿轮 故障诊断 

分 类 号：TH132.41[机械工程—机械制造及自动化]